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| author | Prashant Sharma <prashant.s@imaginea.com> | 2014-01-02 18:41:21 +0530 |
|---|---|---|
| committer | Prashant Sharma <prashant.s@imaginea.com> | 2014-01-02 18:41:21 +0530 |
| commit | 94b7a7fe37a4b1459bfdbece2a4162451d6a8ac2 (patch) | |
| tree | bebd8917d475fdc08e1e3e583be435562e9c4415 /README.md | |
| parent | b810a85cdddb247e1a104f4daad905b97222ad85 (diff) | |
| download | spark-94b7a7fe37a4b1459bfdbece2a4162451d6a8ac2.tar.gz spark-94b7a7fe37a4b1459bfdbece2a4162451d6a8ac2.tar.bz2 spark-94b7a7fe37a4b1459bfdbece2a4162451d6a8ac2.zip | |
run-example -> bin/run-example
Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 4 |
1 files changed, 2 insertions, 2 deletions
@@ -24,9 +24,9 @@ Once you've built Spark, the easiest way to start using it is the shell: Or, for the Python API, the Python shell (`./pyspark`). Spark also comes with several sample programs in the `examples` directory. -To run one of them, use `./run-example <class> <params>`. For example: +To run one of them, use `./bin/run-example <class> <params>`. For example: - ./run-example org.apache.spark.examples.SparkLR local[2] + ./bin/run-example org.apache.spark.examples.SparkLR local[2] will run the Logistic Regression example locally on 2 CPUs. |